Learned Categorical Perception (CP) occurs when the members of different categories come to look more dissimilar (“between-category separation”) and/or members of the same category come to look more similar (“within-category compression”) after a new category has been learned. To measure learned CP and its physiological correlates we compared dissimilarity judgments and Event Related Potentials (ERPs) before and after learning to sort multi-featured visual textures into two categories by trial and error with corrective feedback. With the same number of training trials and feedback, about half the subjects succeeded in learning the categories (“Learners”: criterion 80% accuracy) and the rest did not (“Non-Learners”). At both lower and higher levels of difficulty, successful Learners showed significant between-category separation—and, to a lesser extent, within-category compression—in pairwise dissimilarity judgments after learning, compared to before; their late parietal ERP positivity  also increased and their occipital N1 amplitude  decreased. LPC amplitude increased with response accuracy and N1 amplitude decreased with between-category separation for the Learners. Non-Learners showed no significant changes in dissimilarity judgments, LPC or N1, within or between categories. This is behavioral and physiological evidence that category learning can alter perception. We sketch a neural net model predictive of this effect. The linguists Sapir 1929) and Whorf  suggested that the language we speak shapes the way we see the world. According to this “linguistic relativity” hypothesis, it is learning to put things into different categories by giving them different names that makes them look more distinct to us, rather than vice versa  for Learners but not for Non-Learners . .Effect of learning on the parietal LPC (late positive component). The mean voltage (amplitude) in the LPC window before learning  and after learning  was extracted from a cluster of eight parietal electrodes , and for each individual electrode in the cluster. A two-way mixed ANOVA with time  as a within-subject factor and group (Learners vs. Non-Learners) as a between-subject factor showed a near significant interaction between learning to categorize and changes in the LPC amplitude in the parietal cluster  = 3.867, p = 0.0.058, η2 = 0.108).Post-hoc tests revealed a statistically significant increase in LPC positivity from before to after learning in the chosen cluster  for Learners but not for Non-Learners .Continuous correlational analysis. To complement our discrete analysis (Learner vs. Non-Learner based on an 80% criterion) a continuous correlational analysis treated learning as a matter of degree instead of as all-or-none. Learners and Non-Learners were combined to measure the correlation between their performance (measured as percent correct in categorization on the last learning block) and the ERP changes (after-minus-before differences in N1 and LPC amplitudes).Spearman’s rank-order correlations between the size of the N1 change in the occipital cluster and accuracy in the last block of 100 trials were positive and significant: rho(34) = 0.359, p = 0.034, Fisher z = 0.3769, 95% CI = 0.024–0.622: The better the performance, the smaller the N1 amplitude after learning. The correlation between the change in LPC in the parietal cluster and the accuracy in the last block was also positive and significant (rho(34) = 0.366, p = 0.025, z = 0.3884, 95% CI = -0.012 0.578): The higher the performance, the bigger the LPC amplitude after learning.Experiment 1 revealed two noteworthy ERP effects induced by learning to categorize novel visual textures: a decrease in negativity of an early  component, N1, and an increase in positivity in a late (memory-related) component, the LPC. The data were analyzed in two complementary ways: (i) by partitioning our sample into two populations, Learners and Non-Learners, using a discrete performance threshold of 80% correct and (ii) by treating learning as continuous, combining all subjects and testing the correlation of each of the two ERP components of interest with their learning performance (percent correct) in the last learning block) with. To replicate and build on this initial outcome from exploratory Experiment 1, we did a second experiment using a new independent sample, testing the previous findings as a-priori predictions.Forty-one right-handed subjects  aged between 18 and 35 years were recruited online through Kijiji and the UQAM and McGill Classified Ads website.For this experiment, feature-based a-priori levels were dropped because of feature inhomogeneity in favor of a-posteriori difficulty levels based on the mean accuracy throughout the task. The two stimulus sets that had turned out to be significantly different in terms of accuracy in Experiment 1 (Section 2.2.1) were used: one set that had proven easier, (5/6 covarying features) and one had proven harder (4/6 covarying features).The experiment was conducted in the same way as Experiment 1, including EEG recording. Dissimilarity judgements before and after learning from Perez-Gay et al (2017)  were addIn this second experiment, subjects made pairwise dissimilarity judgments on a subset of forty stimulus-pairs, once before the categorization training and once again after the training. They were presented with a fixation cross (500 ms), after which two stimuli appeared at the center of the screen for 1 s each, one after the other, with an inter-stimulus interval of 1 s. Subjects were then asked to rate their dissimilarity on a scale of 1 to 9, such that 1 corresponded to “very similar” and 9 to “very different”. They were encouraged to make use of the full range of the scale. Of the total of forty pairs presented, 20 were within-category pairs  and 20 were between-category pairs (but of course before training subjects did not know the categories or their names). Responses and reaction times during the task were recorded. The same set of 40 stimulus pairs was presented in the same order for the dissimilarity judgements before and after training. Following the first set of dissimilarity judgements, subjects began their visual category training with corrective feedback, as in the first experiment .EEG data were collected and analyzed in the way described in Experiment 1.EEGLab 13.4.4b open source software  was used to process the EEG files through the steps described in Experiment 1. Having identified our components of interest in the first experiment, the analyses focused on two main components. The first focus was on activity related to early visual processing (N1 component), for the region of interest: Electrodes O1, Oz, O2 and Iz analyzed in the time window between 150 and 220 ms. The other focus was on the Late Positive Component: electrodes CPz, Pz, P1, P2 and POz in the time window between 500 and 800 ms. The mean voltage in the time windows around the peak of each component of interest was extracted. Amplitude differences between conditions were assessed with repeated-measures ANOVA using the IBM SPSS 23 Statistical Software. scalp distributions for each condition in the time-windows of interest were also plotted.Average dissimilarity ratings for within-category and between-category pairs before (“pre”) and after (“post”) training were calculated for each subject, creating the variables “within-category pre-training” (Wpre), “within-category post-training” (Wpost), “between-category pre-training” (Bpre) and “between-category post-training” (Bpost). We created the variable difffW, for “within-category dissimilarity change” (Wpost–Wpre) and diffB for “between-category dissimilarity change” (Bpost–Bpre). Finally, a composite variable “Global CP” (diffB-diffW) was created. Its purpose was to serve as a single joint estimator of CP. Subtracting diffW from diffB will amplify Global CP if diffW and diffB go in opposite directions (within-category compression and between-category separation). If diffW and diffB have similar values, Global CP will be reduced.The significance of these before/after changes was tested with repeated measures ANOVAs with “learning” as the between-group factor and t-tests to compare Successful Learners and Non-Learners separately. Pearson and Spearman correlations were calculated between the Learners’ dissimilarity ratings after-minus-before learning (between categories as well as within) and the corresponding changes in their N1 and LPC.Forty-one subjects completed the visual category-learning task, 21 assigned to the easier (5/6) and 20 to the harder (4/6) level. Twenty-four subjects successfully attained our learning criterion (reach and sustain 80% correct), 16 at the easier level and 8 at the harder level. A one-way ANOVA confirmed that the mean number of trials to criterion differed significantly between the levels  = 11.034, p = 0.004, η2 = 0.408), as did the Learners’ overall accuracy  = 13–242, p = 0.002, η2 = 0.453) and accuracy in the fourth (last) block .Interestingly, among the Learners at the easier level, seven already had an accuracy of over 80% from the very outset of the task. This suggests that, even without explicit instructions to categorize and without feedback, the mere exposure to the 40 pairs of stimuli during the preceding pairwise dissimilarity judgement task had been enough to induce passive learning in these subjects. These subjects were classed as Immediate Learners. Repeated-measures ANOVAs tested the changes in Ss’ Reaction Times and Response Accuracy through the four successive blocks. As in Experiment 1, there was a significant within-subjects effect of successive blocks on both learning measures for the Learners: response accuracy increased linearly across blocks  = 20.178, p<0.01, η2 = 0.801; linear contrast, F = 60.232, p<0.001) and reaction time decreased linearly  = 12.350, p<0.01, η2 = 0.712; linear contrast, F = 18.98, p<0.001). There was no effect for the Non-Learners  = 83.774, p = 0.218, η2 = 0.165; reaction times: Wilks’ Lambda = 0.579, F = 2.907, p = 0.078, η2 = 0.421).A repeated measures ANOVA with difficulty as a between-subject factor for the Learners showed that there was no interaction between difficulty and block for reaction times  = 0.701, p = 0.415, η2 = 0.042) but there was a significant interaction for response accuracy  = 13.731, p = 0.002, η2 = 0.462), indicating that overall accuracy of the Learners was higher at the easier level.Subjects rated pairwise dissimilarity before and after the 400 training trials. If learning to categorize induces CP effects, we expect the dissimilarity score to increase for between-category pairs (between-category separation: positive diffB) and/or to decrease for within-category pairs (within category compression: negative diffW) for Learners only.2 = 0.273. There were three outliers, as assessed by inspection of boxplots. The outliers were kept in the analysis because they did not affect the results as assessed by a comparison of the results with and without the outliers.A three-way mixed ANOVA with time (pre-training vs. post-training) and pair type (between-category vs. within-category) as within-subject factors, group (Learner vs. Non-Learner) as a between-subject factor and dissimilarity judgement scores as the dependent variable was performed to assess the effects of training on perception. There was a statistically significant three-way interaction between time, group and pair type, F = 0.273, p = .002, partial ηPost-hoc tests revealed that Learners rated between-category pairs as significantly “more different” (positive diffB) after training compared to before (diffB)  = 6.453, p <0.01, Cohen’s d = 1.521). Learners also rated within-category pairs as less different after training compared to before (negative diffW), but this within-category compression was not significant  = -1.386, p = 0.184. Cohen’s d = 0.331). For Non-Learners there were no significant changes comparing before and after training, either between or within categories  = 1.935, p = 0.073, Cohen’s d = 0.504; mean diffW = 0.376, t(14) = 1.609, p = 0.130, Cohen’s d = 0.244). Moreover, within-category changes (diffW) went in the opposite direction in Non-Learners, showing (non-significant) within category separation instead of compression. This suggests that mere exposure without category learning induces an overall expansion of the stimulus space in the Non-Learners, instead of the category boundary effects (separation/compression) observed in the Learners.Independent samples t-test assessed differences in the composite “Global CP” variable described in the methods section. The results confirmed that the “Global CP” values were significantly higher for Learners  than for Non-Learners , t(31) = 3.413, p = 0.038.According to a 2-phase neural network model for category learning  some lea2 = 0.367). Post-hoc tests then tested the before-after differences for Learners between the easy and the hard level. While there was significant between-category separation in both the easier  = 5.955, p<0.001, Cohen’s d = 2.714) and the harder condition  = 3.359, p = 0.012, Cohen’s d = 1.194), within-category compression was significant only in the easier condition  = -3.359, p = 0.007, Cohen’s d = -1.134). The harder condition showed only a small, non-significant separation for within-category pairs  = 1.165, p = 0.282, Cohen’s d = 0.428). These results replicate the occurrence of between-category separation in both conditions.The differences between easier and harder conditions were tested with a four-way mixed ANOVA with time (pre-training vs. post-training) and pair type (between category vs. within category) as within-subject factors and group (Learner vs. Non-Learner) and difficulty (easy vs hard) as between-subject factors. There was a significant interaction of all these factors with the dissimilarity judgement scores  = 16.808, p<0.001, ηAs described in Experiment 1, an alternative way to analyze the effects of category learning on perception is to combine all subjects and treat the learning as a continuous matter of degree of accuracy (percent correct) for each subject instead of dividing the subjects into Learners and Non-Learners based on whether they met our (arbitrary) 80% criterion. Across all the subjects combined, all three of our CP measures—(1) the degree of after-minus-before separation between categories (positive diffB), (2) the degree of after-minus-before compression within categories (negative diffW), and (3) GlobalCP (DiffB minus Diff W)–were significantly correlated, in the predicted direction, with our measures of categorization accuracy . Taken all together, these correlations provide robust evidence that the higher the categorization accuracy the greater the CP .For our ERP analysis, we focused on the two time-windows that showed significant effects associated with learning in the first experiment: The N1 and LPC windows. Two borderlines and the 5 Immediate Learners were excluded from the ERP analysis of the Learners. Grand averages were computed for the stimulus-locked ERPs, analyzing the within-subject changes before and after reaching the learning criterion. Using the threshold and statistical methods described in Section 2.1.7, an average of 10.67 epochs (5.56%) of the before-learning trials  and an average of 14.05 epochs, (5.99%) of the after-learning trials  per subject were rejected.For Non-Learners, we analyzed the changes between the first and second half of the training trials. Using the threshold and statistical methods described in Section 2.1.7, an average of 13.36 epochs (6.71%) of the before-learning trials  and an average of 12.64 epochs, (6.30%) of the after-learning trials  per subject were rejected.The mean voltage in a cluster of occipital electrodes  was extracted for N1 and in a cluster of parietal electrodes  for the LPC.This experiment replicated the two main effects observed in Experiment 1: For the Learners, there was a decrease in the occipital N1(150–220) negativity and an increase in the LPC positivity from before learning to after. Both effects were absent in Non-Learners .Effect of Learning on N1: Using the extracted mean voltage in the N1 window for the trials before learning  and the trials after learning  in a cluster of five occipital electrodes , a two-way mixed ANOVA was performed with Time  as a within-subjects factor and Group (Learners vs. Non-Learners) as a between-subject factor. There was a significant interaction between learning to categorize and changes in the N1 amplitude in this occipital cluster  = 4.120, p = 0.0.051, η2 = 0.121).Post-hoc tests confirmed that, for Learners, N1 amplitude decreased from before to after learning . Non-Learners showed no significant changes in N1 between the first and second half of the categorization trials  = 0.213, p = 0.802, Cohen’s d = 0.004). The continuous, correlational analysis combining Learners and Non-Learners showed a nearly significant, positive correlation between accuracy in the last block and changes in the N1 component, . The smaller the N1 amplitude (less negative), the bigger the accuracy in the last block.before the categorization training. A neural network model (to be described in more detail in the discussion section below) suggests an explanation for this unexpected finding: In the nets, which were, like the human subjects, trained to categorize by trial and error with corrective feedback, some learning can already take place during an unsupervised (autoencoding) phase, before the categorization training, through mere repeated exposure, without categorization feedback. Because of this pre-training effect, observed both for the nets and the experimental subjects in Experiment 2, absent in Experiment 1, the averaged ERP waveforms of the Learners during training before reaching our 80% criterion were compared with those of the Non-Learners. It turned out that the Learners’ N1 amplitude on their training trials, before they had reached criterion, already differed significantly from the Non-Learners’ N1 amplitude for the first half of their training trials, based on a between-groups one-way ANOVA  = 7.454, p = 0.010, η2 = 0.199). The only difference between Experiments 1 and 2 is that before their supervised learning  phase the subjects in Experiment were exposed to 40 pairs of stimuli to be rated for Dissimilarity, whereas the subjects in Experiment 1 were not. The N1 difference observed  as a within-subjects factor and group (Learners vs. Non-Learners) as a between-subjects factor used the mean voltage in the LPC window before learning  and after learning  for a cluster of eight parietal electrodes . This analysis showed a significant interaction between learning to categorize and changes in the LPC amplitude in midline electrode Pz  = 4.678, p = 0.039, η2 = 0.135), although the effect did not reach significance in the entire parietal cluster  = 1.708, p = 0.201, η2 = 0.054).The LPC effect was analyzed correlationally on all subjects combined, treating learning as a matter of degree . The conSo far, we have reported ERP effects and perceptual changes induced by category learning separately. A series of Pearson product-moment correlations between the ERP parameters (N1 and LPC) and changes in perceived distance between and within categories was calculated to determine the relationship between the behavioral and the physiological variables.N1 amplitude after learning predicts between-category separation. Tests for a relationship between changes in perceived distance within and between categories and N1 and LPC in Learners showed no significant correlation between either within-category compression (DiffW) or “Global CP” and N1 or LPC. Our main CP effect of interest, however, between-category separation (diffB), correlated positively, strongly and significantly with the amplitude of the occipital N1 after learning : decreased N1 negativity predicted greater separation between categories in Learners. There was no correlation at all for Non-Learners.The Learners’ N1 correlations are shown in The objective of this series of experiments was to test whether CP could be induced by training subjects to sort unfamiliar visual stimuli into two categories through one hour of trial and error training with corrective feedback. We analyzed changes induced by category learning behaviorally (accuracy and dissimilarity judgments) and electrophysiologically  to test whether CP effects (between-category separation and/or within-category compression) are induced by category learning, whether they are perceptual, and what role they play in the mechanisms underlying category learning.Our findings can be summarized as follows: Experiment 1 revealed electrophysiological correlates of category learning: a decrease in occipital N1 amplitude  and an increase in parietal LPC amplitude  in those subjects who had succeeded in reaching our learning criterion (80%) (Learners). There were no effects with the Non-Learners. These effects were replicated in Experiment 2: Categorization training induced between-category separation and significant “Global CP” in the Learners and no effect in the Non-Learners. N1 amplitude also correlated with the size of the perceived between-category separation (a CP effect) after learning (again in Learners only) whereas LPC amplitude correlated with categorization accuracy and Reaction Time. To our knowledge, this study provides the first report of a correlation between an electrophysiological index of category learning and a behavioral index of categorical perception.To learn to categorize requires detecting the features that distinguish the members of the category from the non-members: the features that covary with category membership/non-membership. Experiment 2 replicated a perceptual change that had occurred only in those subjects who had successfully learned to distinguish the members of two different categories after 45 minutes of trial and error training with corrective feedback; the change was absent in those who had had exactly the same training trials but had failed to learn to distinguish the categories. This perceptual change, categorical perception (CP), consisted of an increase in dissimilarity between the members of different categories (“between-category separation”) after successfully learning to categorize them, sometimes also accompanied by a weaker decrease in dissimilarity between members of the same category (“within-category compression”).The direct function of categorization is to differentiate members from non-members, so as to be able to do the right thing with the right kind (category) of thing. This requires selectively detecting the features that distinguish the members from the non-members and ignoring the features that do not distinguish them. Hence increased between-category distinctiveness (separation) would be a direct effect of category learning whereas increased within-category similarity (compression) would only be a side-effect. This may be the reason why between-category separation is stronger than within-category separation in learned CP.Our results also show that unsupervised or unreinforced learning  can also play role in visual category learning. In Experiment 2, the subjects who would eventually turn out to be successful Learners after training were already rating between-category pairs as significantly more dissimilar than within-category pairs before any supervised learning  had begun, hence before they had learned the categories. . Furthermore, the N1 amplitudes before reaching learning criterion were already different from the N1 amplitudes of “Non-Learners” This suggests that the passive exposure during the dissimilarity judgments themselves was already inducing some selective feature detection based on feature frequencies and intercorrelations intrinsic to the stimulus space itself.dimensional reduction [The selective detection of the features that distinguish the members of a category from the non-members can be understood as a form of eduction ,24,58,59This potential explanation for the observed learned CP effect was tested in a neural network model sketched in a previous paper  and is bThis preliminary neural model indicates that CP can be modeled as the difference between the outputs of perceptual filters (i.e. feature-detectors) learned through (1) unsupervised learning and then fine-tuned through (2) supervised learning. There is learning in both phases, but the net starts out as a randomly connected blank slate. Unsupervised learning (mere passive exposure to samples from the infinite space of possible stimuli) allows it to learn the “lay of the land” from the intrinsic frequencies and correlations among the stimulus features, but without any feedback about categorization . Categor−3 on the last layer of the network and nets were labeled as Learner and Non-Learner nets. The model predicts  two of our three principal experimental effects: (a) the human CP effects of between-category separation  in successful Learners difficult or complex, in the sense that there is more irrelevant variation in the data: with all other parameters equal, the network requires more computation to categorize successfully (100% success). The net is hence a potential model for the mechanism underlying category learning. It also supports the hypothesis that dimensional reduction is the mechanism underlying CP: After the supervised learning phase, the hidden unit activations for the N dimensions are transformed, weighting the covarying dimensions more heavily (reduction from N to k).In the nets, reducing the proportion of covariant features (k) makes the task more However, unlike the nets, for which visual stimuli are just N-component vectors of binary features, the human visual system does not process all features equally: some are more salient or detectable than others . TestingOur analysis showed that two ERP components change significantly from before to after reaching the 80% criterion: the Late Positive Component (LPC), which is presumed to be decisional, and the first negative occipital peak , which is presumed to be perceptual. The late parietal LPC peaks between 600 and 800 ms after a stimulus. It is thought to reflect higher-order cognitive processing: conscious recollection , memory-In both experiments, we observed a significant increase in LPC positivity from before to after training only in those subjects who successfully reached our 80% criterion . Given its time course and existing functional interpretation, these LPC changes accompanying category learning cannot account for our perceptual effects. The LPC increase correlated with measures of successful learning: reaction times and decision accuracy. This is consistent with prior findings of a larger LPC for correct responding in a category learning task . OverallThe visual N1 is defined as the first negative occipito-temporal component evoked by any complex visual stimulus, peaking between 150 and 200 ms. It is thought to reflect visual discrimination , featureAnother interesting finding regarding N1 is that whereas the pre-learning N1 values did not differ between Learners and Non-Learners in Experiment 1 (in which only categorization training was done), they differed significantly in Experiment 2, in which subjects did pairwise dissimilarity ratings both before and after training. These differences in Experiment 2 suggest, once again, that before categorization training some of the  Learners were already processing the stimuli differently from the Non-Learners as a side-effect of the passive exposure (unsupervised learning) in making their pairwise dissimilarity judgments. This exposure could already have induced a change in the way subjects processed the features, making the relevant features stand out more for these early and immediate Learners.Category-related effects occurring between 100 and 150 ms have already been reported in the N1 literature in relation to categorization: more N1 negativity for (a) category members than for non-members after learning a category ; (b) forThe studies reporting N1 effects did not test directly for learned CP Effects, however. In the series of studies reported here, the size of the CP (between-category separation) increased as N1 negativity decreased. Because the N1 is a negative deflection, this pattern corresponds to an amplitude reduction: Less negative N1 amplitudes after learning predicted increased CP. This is a correlation between an objective, physiological measure resulting from category learning—a perceptual ERP component—and a subjective measure of perceptual dissimilarity (CP separation). It shows that learning a category can induce changes in perceptual processing even after only a 45-minute training period. Future studies will need to examine the effects of long-term distributed training and overlearning (see below).In an fMRI study of category learning, Folstein reported that learning a category enhanced the discriminability of category-distinguishing features in the extrastriate visual cortex of human subjects . This fiFolstein and others have shown that CP is an effect of “dimensional separation” through attentional weighting of relevant features/dimensions that is not specific to category category boundaries, rather than being a category boundary effect . DimensiThe case of color perception—in which categories are compression bands and boundaries along a single wave-length dimension—is not representative of most learned categories, where it is entire dimensions (features) that are either attended or ignored . BetweenCP effects have been criticized as being artifacts of verbal bias ,77–79. TIt hence seems more likely that it is the process of learning to distinguish the categories that influences their similarity, rather than just the name that is associated with the members once a Learner has succeeded in learning to categorize them . MoreoveOur arbitrary learning criterion was 16/20 (80%) correct across a block of 20 consecutive trials and remaining above that 80% rate for all subsequent blocks . It is likely, however, that the learning in this single 45-minute training session of 400 trials is a matter of degree rather than all-or-none, as the correlational results of our alternative continuous analysis show . MoreoveOur results provide both behavioral and electrophysiological evidence that learning a new category can induce changes in perception. Increases in the perceived difference between categories (between-category separation) after successful learning were more prominent than within-category compression. This makes sense because detecting the differences between members of different categories is more important than ignoring the differences within the same category. Our ERP results suggest that learning a category can influence early stages of visual perception, as indicated by decreased N1 negativity after successful learning. The fact that the size of this perceptual component after learning the category was positively correlated with the between-category separation  further reinforces our evidence that category learning can modify perception. If learning the meaning of words from words, indirectly , is grouS1 FileSupplementary analysis by block of Experiments 1 and 2.(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.


AimThe objective of this study was to compare the efficacy of immunoadsorption (IA) with conventional therapeutic plasma-exchange (cTPE) in ABO-incompatible (ABOi) renal transplant.MethodsData of patients from July 2015 to June 2017  = 11; IA±cTPE) on the average length of stay (ALOS), number of cTPE/IA, antibody-titers (AT), creatinine, patient and graft survival at one year were compared retrospectively with patients in period from February 2012 to June 2015 . AT of patients not decreasing to less than one fold after two cTPE were shifted for IA. For patients undergoing IA, real-time AT was done and IA stopped after target titer (TT <1:8) was achieved. Post-transplant cTPE was done if, titers rebounded to ≥1:8. Intravenous immunoglobulin (IVIG) was given after every cTPE/IA. Cost comparisons were made.Resultsp = 0.206). The mean IA procedures in category-I are 1.6 ± 0.5. The number of patients requiring post-operative TPE was less in category-I than category-II, i.e., N = 5, 45.5% vs N = 20, 69%, respectively (p = 0.171). The expense of IA in category-I vs cTPE in category-II was statistically not significant (p = 0.422) but had significant lesser ALOS (p = 0.044). Expenses, when a patient undergoes both cTPE and IA (category-I), are significantly higher to category-II (p = 0.003). The two groups were comparable in AT, creatinine value, graft and patient survival rates at one year.In category-I, seven patients (63.63%) were shifted to IA from cTPE. The mean cTPE procedures in category I and II are 3.5 ± 2.4 and 4.8 ± 2.5, respectively (ConclusionContrary to the general judgment of IA being expensive than cTPE, this study shows equivalent expenditures with comparable therapeutic outcomes. Significant improvements in immunosuppression and desensitization protocols led to favorable or even comparable patient outcomes between ABO-compatible and ABO-incompatible renal transplants (ABOi) , 2. AmonImmunoadsorption (IA) is a more selective and antigen-specific protocol, that uses columns made of low-molecular-weight carbohydrates with immobilized blood-group A or B antigens linked to a sepharose matrix. These columns specifically deplete anti-B or anti-A antibodies corresponding to the antigen present on it, thereby making it more effective. This protocol has gained traction over the years and is successfully implemented in many well-established centers across the world -6. HowevIn view of the limited studies documenting the use of this modified protocol, we aimed to compare the efficacy and cost-effectiveness of using goal-directed IA (with the re-use of IA columns wherever needed) over cTPE in achieving the target ABO titers in live related ABOi renal transplants. In this study, all consecutive patients with chronic kidney disease (CKD) stage IV/V and undergoing ABOi renal transplants at our tertiary care hospital of northern India between 2012 and 2017, were included. All renal transplants were done after obtaining proper informed consent and appropriate histocompatibility workup.Immunosuppression2) in pre-operative period, followed by tacrolimus (TAC) 0.05 to 0.075 mg/kg twelve hourly and mycophenolate mofetil (MMF) 1 g/day in divided doses, from the day of surgery. Methylprednisolone was started with 1 gm intravenous dose during the intra-operative phase and continued with 100 mg/day and 80 mg/day on post-operative day (POD) one and two followed by switching and tapering with prednisolone. A trough level of 8-12 ng/mL was maintained for TAC in the first month of the post-operative period and tapered after that to 5-8 ng/mL  and category II (cTPE exclusively). cTPE was performed on Hemonetics MCS+ . One to one and a half plasma volume was processed per session with five percent albumin as replacement fluid. Two units of group AB, fresh frozen plasma was used as the replacement fluid towards the end of the procedure to maintain the coagulation profile. IA was done using antigen-specific immunoadsorbent column  on Terumo BCT's Spectra Optia®.Antibody titersCombined antibody titers  were done at the baseline, after each cTPE &/or IA and daily till discharge in both the categories. Polyspecific antihuman globulin (AHG) phase gel card was used for all patients in category I and tube technique was used for all the patients in category II. Though the AT detection methods were different in both the groups, the study focused on the ‘titer reduction' per session of cTPE or IA, rather than the titer technique per se. Moreover, the AT detection method remained the same for all patients pertaining to a single category. For patients undergoing IA, real-time goal-directed AT was done  and the Patient and graft survivalAntibody-mediated rejection (AMR) was assessed on the basis of renal function tests. Kidney biopsies were performed exclusively on the patients who showed features of graft dysfunction. Patient survival was calculated from the date of transplantation to the date of death. Graft survival (non-censored for death) was calculated from the date of transplantation to the date of irreversible graft failure signified by a return to long-term dialysis or re-transplantation or date of death. Death of patient despite having a functional graft was treated as graft failure.Cost comparisonThe average costs for desensitization procedure, IVIG and bed charges/day of tertiary care corporate centers in North India was considered for the cost comparison namely: Mean bed charges/day as 71.52 USD/day (42.91-100.12 USD/day); IVIG cost as 143.03 USD/dose (100.12-185.94 USD/dose); cTPE with replacement fluids as 715 USD/session (500.61-929.69 USD/session), cost for processing eight plasma volumes by using an IA column as 4791.51 USD (4147.87-5435.14 USD) and additional cost of 286.06 USD (214.54-357.57 USD) for the second session of IA using the same column The length of stay was considered the same as the number of preoperative cTPE and/or IA days only for cost comparison purpose.Statistical analysisData on patient demographics, combinations of ABO-incompatibilities, rituximab conditioning, the average length of stay (ALOS); the number of cTPE/IA procedures; antibody titers (AT) at the baseline, transplant and discharge; serum creatinine at discharge, patient and graft survival after one year were prospectively collected for category I and compared with the data of the category II . The ALOTests were done using the statistical package for the social science system version SPSS 20.0 and R-3.2.0. Continuous variables are presented as Mean ± SD (Standard Deviation), and categorical variables are presented as absolute numbers and percentage. Chi-Square test was applied to observe the correlation between categorical variables and T-test was used to compare the differences in continuous variables between category I and II. Z-test of proportion was used to compare the proportion of parameters between the two categories. For all statistical tests, a p-value less than 0.05 was considered clinically significant.There were eleven patients  in the category I with mean age 31.27 ± 13.5 years. Abiding by the set protocol, seven of the eleven patients (two patients with anti-A and five patients with anti-B antibody titer) were started on cTPE and shifted to IA due to the resistant titers. Four of them were directly started on IA in the category I, owing majorly to the patient affordability. One of these four patients was switched to cTPE after IA due to the resistant titer of anti-A antibody. In category II, there were twenty-nine patients. Of them, twenty-three (79.3%) were males and six (20.7%) females with an average age of 39.03 ± 14.53 years. All of these patients underwent cTPE for reduction of AT.p = 0.127) and gender distribution (p = 0.859). The ABO incompatible donor-recipient combinations in both categories are detailed in Table The two categories were comparable in terms of age p = 0.27 and geComparison of the number of procedures; ALOS, AT at baseline, transplant, and discharge; creatinine values; the patient and graft survival in one year are shown in Table p = 0.206). Mean IA procedures in the category I was 1.6 ± 0.5.In the pre-transplant period, mean cTPE procedures in the category I and II were 3.5 ± 2.4 and 4.8 ± 2.5, respectively (p = 0.171) though it did not reach statistical significance. The two groups were comparable in AT at all times, creatinine value, graft and patient survival rates after one year  table  but had 2 table  Two patients (18.2%) in the category I succumbed to death within the first three weeks post-transplantation owing to fungal granulomas in brain parenchyma and septic shock. Among the survivors, two (22.2%) patients had graft dysfunction. The first patient showed chronic inflammatory cell infiltrates in the interstitium, focal tubulitis, patch areas of moderate interstitial fibrosis with tubular atrophy and on graft biopsy. C4d staining was negative. Overall, the picture is suggestive of acute on chronic rejection. After graft biopsy, the second patient showed patchy fibrosis in the interstitium; atrophic tubules in the scarred area and thick basement membranes. Interstitium had lymphocytic infiltrate which was severe in areas of fibrosis. Mild to moderate lymphocytic tubulitis was seen. C4d staining was seen in 85% of peritubular capillaries. All these features were suggestive of acute cellular rejection in a background of chronic allograft nephropathy.Two patients (6.9%) in the category II succumbed due to sepsis. Of the survivors, one experienced acute humoral rejection within twenty-four hours of transplant. The second patient faced graft dysfunction within the first week of transplant. Graft biopsy revealed neutrophil infiltrates in the glomeruli and peritubular capillaries. There were tubular injury and C4d staining of 30%. This patient was salvaged with cTPE and other medications. The third patient succumbed due to TMA  related complications in spite of cTPE procedures and supportive drugs. The biopsy described coagulative necrosis and hemorrhage in the cortical tissue with the absence of viable glomeruli. There was patchy interstitial edema, congestion of peritubular capillaries, patchy infiltrates, and hyaline casts, suggestive of acute AMR.This study presents the comparison between two modalities of antibody titer reduction for ABO-incompatible renal transplant i.e. IA with or without cTPE (category I) and only cTPE (category II). The average number of procedures, ALOS, post-transplant procedures, patient and graft survival after one year of transplant were compared. As per the analysis, the ALOS of the patients in the category I was eighteen days. This was found to be significantly different from that of the category II where the average length was of twenty-seven days. Other parameters like the average number of procedures, procedures performed after the transplant, patient and graft survival after one year of transplant were found comparable among the two groups. The other aspect explored was the finances involved, which has continued to be a major concern for non-insured patients. The study demonstrates a significant difference between the expenditure with patients who were switched over to IA from conventional TPE owing to resistant titers; as compared to the patients who underwent conventional TPE only. However, the expenses for IA only were found comparable to the expenses for undergoing exclusive cTPE.Schwenger et al. described the various indications for the use of IA columns . HoweverA similar comparison between IA and cTPE has also been attempted to assess the AT reductions in ABOi renal transplant by Parmentier et al. . They coTyden et al. discussed outcomes in eleven patients undergoing ABOi renal transplant . The IA Alongside these advantages, not using replacement fluids like albumin/fresh frozen plasma (FFP) in IA leads to fewer donor exposures and maintains the immunoglobulin levels.p = 0.003).Koo et al.  mention As explained in another study by Tyden et al. , the earThe median titers in both the groups before the transplant, at transplant and discharge were comparable. The serum creatinine values, patient and graft survival were comparable among the groups showing the clinical efficacy of either of the procedures implemented are similar.In the present study, two and one patient from the category I & II respectively succumbed due to infection and sepsis. There have been conflicting and inconsistent findings with regard to infectious rates with or without rituximab and plasma exchange desensitization regimens. Some of them support the finding of higher overall infection rate in ABOi transplants as compared to that of ABO-compatible transplants whereas others contradict -23.Our study was not without limitations. To start with, comparison for the number of the fold of decrease in antibody titer by IA alone vs cTPE was not performed because of a statistically limited sample size number (N = 4) for patients undergoing only IA without prior cTPE. The complications and infection rate related to the ALOS in both categories were not evaluated. The choice of IA was dependent solely on patient affordability. Lastly, a comparison between the equipment used for cTPE and IA was not considered.The efficacy of the goal-directed extensive use of IA column was as efficacious as the cTPE. Moreover, it has an added advantage of decreased ALOS, thereby cutting down the costs and making it an affordable approach and improving the quality of life of the patients.


Htr2a in the DCN, the gene encoding 5-HT2A receptor, was shown to be a hub gene in the protein-protein interaction network identified using RNA-seq. This indicated that 5-HT2A receptor-mediated signaling may be responsible for DCN-dependent functional improvement in EE. We further verified this using the 5-HT2A receptor antagonist, MDL100907, to inhibit the function of 5-HT2A receptor in the DCN. This treatment resulted in impaired recovery in EE treated mice, who performed at a level as poor as the stroke-only group. Thus, this work contributes to an understanding of the importance of the DCN activation in EE-induced post-stroke rehabilitation. Attempts to clarify the mechanism of 5-HT2A receptor-mediated signaling in the DCN may also lead to the creation of a pharmacological mimetic of the benefits of EE-induced rehabilitation.The level of cerebellar activity in stroke patients has been shown to correlate with the extent of functional recovery. We reasoned that the cerebellum may be an important player in post-stroke rehabilitation. Because the neurons in the deep cerebellar nuclei (DCN) represent virtually all of the output from the cerebellum, in this study, using environmental enrichment (EE) to promote rehabilitation, we investigated the influence of the optogenetic neuronal modulation of DCN on EE-induced rehabilitation. We found that neuronal inhibition of the DCN almost completely blocked motor recovery in EE treated mice, but the stroke mice with neuronal activation of the DCN achieved a similar recovery level as those in the EE treated group. No difference was observed in anxiety-like behavior. Moreover,  Stroke is a leading cause of disability worldwide . NumerouThe cerebellum is structurally connected to the sensorimotor cortex via the corticopontocerebellar tract (CPCT). Considerable evidence has implicated the cerebellum as an important node in the brain networks that are altered following supratentorial stroke , which rEnvironmental enrichment (EE) is a classical but effective therapeutic intervention for stroke rehabilitation in rodents. In comparison to a standard environment, EE provides comprehensive sensory, motor, cognitive and social stimulation . In receIn the present study, we aimed to examine the role of the DCN in EE-induced functional recovery following stroke. Specifically, we investigated whether optogenetic neuronal modulation of the DCN, the sole output nuclei of the cerebellum, influenced the efficacy of EE on post-stroke recovery. We also sought to determine the molecular changes within the DCN after exposure to EE using RNA-seq. The candidate molecules revealed in this study will inform future attempts to create a pharmacological mimetic of EE, with the ultimate goal of improving post-stroke rehabilitation.All experimental procedures were approved by the Animal Ethics Committee of the Fudan University . Male C57BL/6J mice  were used in this study. All mice were housed in a 12:12 h light:dark cycle (with 08:00 to 20:00 as light-on hours), in a room with controlled temperature and humidity. All mice were anesthetized by intraperitoneal injection of 1% pentobarbital (50 mg/kg). During surgery, body temperature was maintained at 37°C with a heating pad.PT was induced unilaterally in the sensorimotor cortex. Briefly, anesthetized mice were placed in a stereotaxic device (RWD Life Sciences) with the skull exposed. The entire skull was then covered by an opaque template which exposed a circular region of the left sensorimotor cortex . Immediately after intravenous injection of 0.1% Rose Bengal , the sensorimotor cortex was illuminated with a 532 nm green laser beam (50 mw) for 10 min at maximum output.Anesthetized mice were fixed into a stereotaxic device using ear bars. To prevent any corneal damage, ophthalmic ointment was used. A burr hole was drilled into the right side of the cerebellum, and a glass electrode filled with virus was aimed at the DCN. The intended mouse brain DCN stereotactic coordinates, as previously described , were asMice received photostimulation beginning on the 7th day post-PT surgery. Stroked mice were placed in an empty cage to allow for free movement with the laser cable connected to the fiber cannula. One session of stimulation consisted of three 1 min stimulations separated by 3 min rest intervals. All of the mice, including the sham-stimulated stroked mice, received daily (7 days/week) stimulations for 2 weeks post-stroke. In the ChR2 gene expression group, a 473 nm blue laser was controlled by a driver , and mice were stimulated with a laser set to 10 Hz, with 20 msec light pulses. In the NPHR gene expression group, a 594 nm yellow laser was controlled by the same driver as above, whereas mice were stimulated with direct current. The laser power operated with a range of 1.2-1.4 mW, which was measured by a power meter before implantation. Stimulations were performed in the morning between 12:00 and 13:00. Sham stimulation (SS) was conducted without turning on the laser.The day after the final behavioral test, mice were anesthetized and then transcardially perfused with 4% paraformaldehyde. The regions of virus injection were confirmed under a fluorescence microscope. Data from mice with an absence of ChR2 or NPHR gene expression were excluded from the group.A burr hole was drilled on the right side of the cerebellum. Another two holes were drilled near the implantation point for screw insertion. The cannula was then inserted into the DCN and fixed with dental cement. For subsequent pump installation, a 2 cm long PE50 catheter, filled with sterilized artificial cerebrospinal fluid (aCSF), was inserted into the angle arm of the cannula, and the other end of this catheter was sealed for later pump connection. The sealed end of the catheter was put into the pocket made between the shoulder blades of the mice. Mice were allowed to recover for 14 days before they received PT surgery.th day post-PT surgery. Osmotic pumps were filled with MDL100907  and kept in sterile distilled water at 37°C overnight before installation =52.90, p<0.0001), treatment  and time × treatment interaction  were observed using a two-way ANOVA test with repeated measures. Upon further analysis with a Tukey’s multiple comparisons test, we found no obvious distinction between groups in the pre-trial training (p=0.78). However, compared to the sham group, serious motor deficiencies were observed in the PT group on the 7th day post-PT, with an approximately 60% reduction in the rotarod travel time. As EE started from the 7th day post-stroke onwards, better performance was observed in the PT+EE group compared to the PT group on both the 14th (p=0.004) and 21st (p<0.0001) day post-PT .A rotarod test was used to assess motor coordination ability . SignifiF=15.00, p<0.0001), treatment  and time × treatment interaction  among the three groups. On the 7th day post-PT, a marked deficit in the use of the right forelimb and a higher reliance on the contralateral left forelimb was observed in stroked mice of both PT and EE groups. However, on the 14th day post-PT the EE treated group tended to touch the cylinder wall more often with both paws (p=0.028), and less often with their left forelimb only (p=0.002), in comparison to the PT group. Both PT and PT+EE groups recovered gradually, and by the 21st day post-stroke the two groups displayed equal paw placement times .Additionally, the cylinder test  and rungF=4.94, p=0.003), treatment  and time × treatment interaction  on forelimb placement among the three groups .Similarly, a two-way ANOVA with repeated measures indicated significant effects of time . This indicated an attenuating role of EE on anxiety-like behavior. However, as no remarkable differences were observed between PT and sham groups, the PT stroke model appeared to have no effect on anxiety-like behavior. Additionally, the time spent in the center zones .In order to evaluate the exploration ability or anxiety level of mice, we analyzed EPM data by comparing the time spent in each maze zone . Compareer zones  was virtTo further investigate the role of the DCN in EE-induced recovery after stroke, we used optogenetics to modulate DCN activity, allowing us to simultaneously observe the resulting behavioral changes .F =68.60, p<0.0001), treatment  and time × treatment interaction  were found, as assessed using a two-way ANOVA with repeated measures. Upon further analysis with a Tukey’s multiple comparisons test, we found that, compared to the PT+SS group, repeated DCN activation resulted in an increased latency to fall in the PT+ChR2 group (p=0.01) on the 21st day post-PT, which was as long as that of the PT+EE+SS group (p=0.56). However, compared to the performance of the PT+EE+SS group , when the PT+EE+NPHR group was given DCN inhibition, mice exhibited significantly slower and poorer recovery rates, at the same level as those observed in the PT+SS group . No significant difference was found between the PT+SS and PT+NPHR groups .In the rotarod test , signifiF=29.30, p<0.0001), treatment  and time × treatment interaction  were also analyzed using a two-way ANOVA with repeated measures. Upon further analysis with a Tukey’s multiple comparisons test, the PT+ChR2 group was found to have a better rate of recovery than the PT+SS group on the 14th day post-PT (p=0.001), although not better than that of the PT+EE+SS group (p=0.45). However, by the 21st day post-PT, a similar performance was observed in the PT+ChR2 and PT+SS groups (p=0.94). Like the observed rotarod behavior, repeated DCN activation in the PT+EE+ChR2 group did not facilitate significant changes in forelimb asymmetry when compared with results from the PT+EE+SS group . Nevertheless, when the PT+EE+NPHR group was treated with DCN inhibition, mice exhibited a robust deterioration in the use of the paralyzed forelimb compared to that of the PT+EE+SS group by the 14th day post-PT (p=0.01), which was as poor as that observed in the PT+SS group . Likewise, no significant difference was found between the PT+SS and PT +NPHR groups .In the cylinder test , the effF=46.30, p<0.0001), treatment  and time × treatment interaction  were again analyzed using a two-way ANOVA with repeated measures, with further analysis by a Tukey’s multiple comparisons test. Repeated DCN activation resulted in more improvement in the PT+ChR2 group than in the PT+SS group on the 21st day post-PT (p=0.02), which was equal to that of the PT+EE+SS group (p=0.89). Moreover, repeated DCN activation in the PT+EE+ChR2 group did not facilitate improved recovery relative to the PT+EE+SS group . However, DCN inhibition in the PT+EE+NPHR group resulted in a slower and poorer recovery compared to the PT+EE+SS group on the 21st day post-PT (p=0.01), which was identical to that observed in the PT+SS group (p=0.99). No significant differences were found in the number of hindlimb slips .In the rung walking test , the effmb slips  among thF=6.99, p=0.0001; closed arm: F=6.38, p=0.0002). There were no significant differences found among all groups in the amount of time spent in the center zone of the maze .To identify the potential involvement of the DCN in the EE-induced attenuation of anxiety-like behavior, we again used optogenetics to modulate DCN activity and recorded the time spent in the EPM test . In lineHtr2a, which encodes 2A5-HT receptor, a sub-type of serotonin receptors responsible for motor coordination in the cerebellum, tended to be a hub gene in the PPI network. To validate the RNA-seq data, we further tested the selected mRNAs from the list of upregulated genes against the same RNA as that from RNA-seq experiments, which showed consistent changes with the RNA-seq data  via RT-PCR. Using a two-tailed t-test, a significantly (~50%) higher Htr2a expression was observed in the PT+EE group compared to the PT group , whereas no difference was observed between the sham and PT+EE groups .Next, we examined the expression levels of MDL100907, a selective 2A5-HT receptor antagonist, in order to inhibit the receptor’s activity in the affected DCN, and further examine the role of the DCN 2A5-HT receptor in EE-induced recovery after stroke , treatment  and time × treatment interaction  were observed using a two-way ANOVA with repeated measures. Further analysis with a Tukey’s multiple comparisons test indicated that, compared to the PT+EE+PBS group, the latencies to fall were considerably decreased in the PT+PBS, PT+MDL100907 and PT+EE+MDL100907 groups. Even given the presence of EE treatment, no improvement in performance was observed in the PT+EE+MDL100907 group compared with the PT+PBS group on both the 14th (p=0.95) and 21st (p=0.98) days post-stroke. Performance of the PT+EE+MDL100907 group was significantly worse than the PT+EE+PBS group . No differences were found between the PT+PBS and PT+MDL100907 groups .In the rotarod test , signifiF=26.10, p<0.0001), treatment  and time × treatment interaction  were analyzed using a two-way ANOVA. Results definitively demonstrated an inhibition of EE-induced recovery by MDL100907. Compared to the PT+EE+PBS group, mice in the PT+EE+MDL100907 group tended to rely more on their left forelimb (p=0.001) on the 14th day post-stroke. Nevertheless, MDL100907 seemed to have no effect on PT, as the PT+MDL100907 and PT+PBS groups exhibited similar performance in limb asymmetry . By the 21st day post-stroke, all of the groups exhibited similar forelimb asymmetry .In the cylinder test . The effF=27.40, p<0.0001), treatment  and time × treatment interaction  on forelimb placement among the three groups. No significant differences were found in the number of hindlimb slips (MDL100907 group still had a significant number of forelimb slips on the 21st day post-stroke (p=0.04), meaning MDL100907 was able to block the effect of EE on this behavior test. Since no statistically significant difference was found between the PT+PBS and PT+PBS+MDL100907 groups (p=0.99), we may conclude that MDL100907 administration had no influence on the natural time-course of PT recovery .In the rung walking test , a two-wmb slips . Althoug2A5-HT receptor is involved in motor recovery, we further sought to examine whether it plays a role in the EE-induced attenuation of anxiety-like behavior in the EPM test , mice spent more time in the open arm . Among all groups, we found no significant differences in the amount of time spent in the center zones of the maze .Because the EPM test . A one-wopen arm , and decopen arm , compareHtr2a in the DCN, the gene encoding 5-HT2A receptor, was identified as a hub gene in our PPI network. This indicates that 5-HT2A receptor-mediated signaling in the DCN may be necessary for the ability of EE to enhance motor function post-stroke. This work furthers our understanding of EE-induced recovery following stroke and emphasizes the appreciable role of the DCN in the process of rehabilitation.In the present study, we sought to reveal the contributions of the DCN to EE-induced post-stroke rehabilitation. We demonstrated that the ability of EE to promote post-stroke motor function was definitively dependent on the DCN activation. Furthermore, In our work, EE resulted in the greatest enhancement in the rotarod test, which is a reliable assessment of cerebellum-dependent motor coordination , compareInterestingly, in contrast to previous studies using electrical or optogenetic stimulation of the lateral cerebellar nucleus to improve motor recovery , 15, 16,Furthermore, our data provides further evidence for the DCN engagement in cortical functional plasticity. As we know, the cerebellum is structurally connected to the sensorimotor cortex, and the activity and excitability of the cerebral cortex is dependent upon ascending input originating from the contralateral DCN . PreviouHtr2a, a gene encoding the serotonin receptor subtype 5-HT2A receptor, to be differentially expressed in the DCN in post-stroke animals receiving EE. Furthermore, we found that 5-HT2A receptor-mediated signaling was responsible for EE-induced functional recovery  has been reported to engage in a variety of physiological functions, including affective responses and cognitive processing . In thisrecovery . Unexpecrecovery , withoutrecovery . Indeed,out mice , 23, 5-Hfunction -26. Becarebellum , 28, but5-HT2A receptor-mediated signaling may be responsible for DCN-dependent functional improvement in EE. These results suggest that future therapeutic approaches that aim to modulate the DCN activation may be crucial for effective rehabilitation. Moreover, further attempts to clarify the role of 5-HT2A receptor-mediated signaling in the DCN may lead to the creation of a pharmacological mimetic of the benefits of EE-induced post-stroke rehabilitation.In summary, we show here that the DCN activation is indispensable for EE-induced motor promotion after stroke, without being involved in the EE-induced attenuation of anxiety-like behavior. Additionally, we found that


A dyeing bath consisting of a micro-emulsion system involving a small proportion of n-butyl acetate was used, and the kinetics of dyeing were analysed at four temperatures . Moreover, the dyeing rate constants, correlation coefficient and activation energies were proposed for this system. It was found that o-vanillin yielded higher dye absorption levels than coumarin, leading to exhaustions of 88% and 87% for Disperse Red 167 and Disperse Blue 79, respectively. K/S values of dyed polyester were also found to be higher for dye baths containing o-vanillin with respect to the ones with coumarin. In terms of hot pressing fastness and wash fastness, generally no adverse influence on fastness properties was reported, while o-vanillin showed slightly better results compared to coumarin.This work focused on the evaluation of the kinetics of dyeing polyester fabrics with high molecular weight disperse dyes, at low temperature by solvent microemulsion. This study also compared the effect of two non-toxic agro-sourced auxiliaries ( Tg) of around 80 °C, make their dyeing process challenging, since it is high-energy and consumes a significant amount of water [Tg. However, the nature and high molecular weight of the organic compounds used generally as carriers involves problems of non-biodegradability and toxicity. These agents can also partially plasticize polyester fibres, decreasing their glass transition temperature [Polyester  (PES) fibres are the most used fibres in the global apparel industry, and therefore are a prominent topic of research. The hydrophobic nature PES fibres, combined with a glass transition temperature ). This method was free from the aforementioned disadvantages of carriers, but its industrial application is costly and very difficult to perform [Recent studies have attempted to look for alternative processes for polyester dyeing with non-hazardous and more environmentally friendly compounds that also require less energy in the overall dyeing process. With the aim of avoiding negative effects of the carriers, Harifi and Montazer  studied  perform . Choi et al.  comparedo- and p-vanillin as carriers for low-temperature dyeing of polyester fabrics (90 °C), with low and high molecular weight disperse dyes in the presence of ethanol as a co-solvent [o- and p-vanillin can be a substitute for conventional carriers. Additionally, PES fabric dyed with these carriers presented acceptable absorption and fastness. Furthermore, Pasquet et al. [o- and p-vanillin with the USEtox system, confirming that these carriers are not toxic. Carrión-Fite proposed a low-temperature dyeing process in the presence of a micro-emulsion obtained by ultrasonic agitation, composed of a low proportion an alkyl halogen solvent and a phosphoglyceride emulsifier [On the other hand, several studies propose dyeing processes with more environmentally friendly carriers at temperatures lower than 100 °C. Pasquet et al. [ulsifier . In thisulsifier ,13.o-vanillin or coumarin in an n-butylacetate co-solvent (an organic non-toxic solvent) for low molecular weight disperse dyes (C.I. Disperse Blue 56 and C.I. Disperse Red 73). The study confirmed that o-vanillin and coumarin had a positive and significant influence on dyeing behaviour of polyester in this system. At a temperature of 95 °C, the absorption levels of the fibres after 120 min were excellent: the content of Disperse Blue 56 dye absorbed by the PES in the presence of o-vanillin or coumarin was 97% and 96%, respectively. In the case of Disperse Red 73, the amount absorbed was 81% and 82%, respectively, for of o-vanillin or coumarin. Moreover, wash fastness was similar to the conventional process of dyeing polyester [More recently, Carrión-Fite and Radei  proposedolyester ,15. Howen-butyl acetate), and two potential auxiliaries (o-vanillin and coumarin). Moreover, the activation energies were determined for this system. In this paper, we investigated the kinetics of dyeing PES fabrics with two high molecular weight disperse dyes (Disperse Red 167 and Disperse Blue 79), in a micro-emulsion system involving a small proportion of a non-toxic organic solvent (o-vanillin (99% purity) was supplied by Acros . Both carriers use agro-sourced alternative compounds with additional properties, such as antimicrobial, antioxidant, biodegradability and antimutagenic. Also, these carriers are hydrophobic with no toxicity, and we expect them to have better influence on polyester dyeing [The fabric used was a PES 100% woven standard polyester, Type 30 A, from Testgewebe GmbH  (ISO 105-F10). The high molecular weight disperse dyes used were Rubi Foron S-2GFL (C.I Disperse Red 167) and Navy Blue Foron S-BRL (Disperse Blue 79). All dyes were supplied by Clariant . Coumarin (99% purity) was supplied by Acros  and n-butyl acetate (Mw = 116.16 g·mol–1), supplied by Panreac , was used as the co-solvent. N,N-Dimethylformamide (Mw = 73.10 g·mol–1), 99.8% purity and supplied by Panreac , was used as the solvent to extract the dyes from the PES fabrics. Hostapal non-ionic surfactant detergent, supplied by Archroma GmbH , was used as for pre-dyeing washing. QP-grade 85% pure sodium dithionite (wM = 174.11 g·mol–1) and analytical-grade sodium hydroxide, both supplied by Panreac, were used as a post-dyeing washing reductant.Pure –1 concentration at 40 °C for 30 min.PES fabrics were prepared for dyeing by cutting 16 cm in warp and 11 cm in weft, and weighing 3.0 g. Fabric pre-washing was done by using Hostapal detergent at a 0.5 g·Ln-buthylacetate and then adding the dye solution. The mixture was stirred for 2 min followed by sonication for 1 min. The bath ratio for each dyeing operation was set to 1:60. The quantity of dyes and chemicals used were 2% o.w.f. of disperse dye, 4% o.w.f. of auxiliaries and 3 ml of n-buthylacetate, for a total amount of 180 mL of dye bath. The dyeing process was carried out in a Linitest dyeing apparatus, furnished with 300 mL sealed cans that were purchased from Atlas MTT GmbH . The dyeing temperatures were 83, 90, 95 and 100 °C for a fixed time of 120 min. One-half of all tests used o-vanillin and the other half coumarin as auxiliaries.The dye bath was prepared by dissolving carriers in After the dyeing process, the samples were rinsed with water two times and dried at ambient conditions. Post washing was performed in a reductive medium to remove all dye absorbed onto the fibre surfaces. The reductive medium consisted on a mixture of 0.5 g/L sodium hydroxide and 2 g/L sodium hydrosulphite in distilled water (bath ratio 1:50). This process was followed by washing at 50 °C for 30 min, rinsing three times with water and drying at room temperature for 24 h. Wash fastness of the samples was tested according to the UNE-EN ISO 105-C06 method. The samples were washed with IEC 60456 Base Detergent Type A at 40 °C for 30 min, keeping liquor to material ratio as 1:50. The colour fastness to hot pressing was tested according to the UNE-EN ISO 105-X11 method. N,N-dimethylformamide (DMF). The absorbance at the maximum wavelength in the visible spectrum is a linear function of the concentration of dye dispersed in DMF following the Beer–Lambert law. The maximum absorbance was determined for each dye at different concentrations, obtaining linear regressions with R values ranging from 0.9758 to 0.9845. A model M 330 UV–Visible spectrophotometer was used that was purchased from Camspec .The amount of dye absorbed by the PES was quantified by spectrophotometric analysis after dye extraction with To evaluate the homogeneity of the dye baths, particle size measurements were performed using a Zetasizer nano series device from Malvern  at 90° with respect to the incident laser beam at 25 °C .C0 is initial concentration of dye and sC is final concentration of dye.The exhaustion was determined using Equation (1):K/S value, was determined from the spectral diffuse reflectance using a spectrophotometer (Gretag Macbeth 7000). Reflectance values of the samples (R) were used to calculate K/S based on Kubelka-Munk, as shown in Equation (2) [R is the % Reflectance/100 of coloured samples at λmax, and K and S are the absorption and scattering coefficients, respectively.The colour strength, expressed as tion (2) :(2)KS= and the mean of polydispersity (0.2) presented standard values for the dyeing process. Particle sizes of dye-bath in a presence of coumarin showed better results. Indeed, presence of this auxiliary successfully reduced the particle size. Nonetheless, when o-vanillin KT=X under pressure to measure colour changes and colour migration to the white fabric due to the steam. Generally, heat fastness of this dyeing method was positive, and no significant changes can be seen for the properties. Degradation hot pressing fastness of both dyes and auxiliaries at lower temperatures, like 83 and 90 °C, were in the regular level (such as 2–3 or 1–2), whereas discharge hot pressing fastness were in the excellent level, as most values were 5. Polyester fabric dyed in this process had good heat stability based on the degradation results. In terms of transforming colour (migration) to the white fabric (discharge hot fastness), the results were in the regular level.O-vanillin showed remarkably better heat fastness than coumarin, and values obtained with o-vanillin were more convincing. In terms of two disperse dyes, the results indicated that C.I. Disperse Blue 79 was better than C.I. Disperse Red 167.K/S analysis, it was found that both auxiliaries at temperatures near to 100 °C led to a good dyeing absorption level in PES fabrics.Based on the kinetics and o-vanillin yielded, in general, a higher absorption level and colour strength than in the presence of coumarin. Moreover, a higher colour strength was found for Disperse Red 167 compared to Disperse Blue 79. It is understandable that temperature would have a significant influence in this dyeing system. The effect of temperature on o-vanillin was more apparent, as dyeing PES with o-vanillin involves more energy to penetrate the polyester fabric than coumarin. Ranges of energies were similar to those achieved in the conventional process. In terms of applicability and interaction between fabric and dye molecules, the swelling of polyester dyed with o-vanillin auxiliary was higher than coumarin; therefore this factor led the fabric to absorb more colour. According to wash and hot-pressing fastness, the auxiliaries were almost similar to each other. Fastness property results were positive, and there were no significant changes of the fastness properties. However, polyester fabric dyed with o-vanillin show slightly better wash and heat fastness than coumarin. Comparing the two auxiliaries studied, it was found that polyester dyed in the presence of Additionally, we attempted to dye polyester in a manner as ecologically friendly and economical as possible. The percentages of auxiliaries and dyes were at a minimum level, and this method had several advantages, such as low-cost materials, low energy costs, no toxic chemicals, no deformation of the fabric due to low temperature and the possibility of reusing the waste water as a fertiliser. Therefore, this process can be suggested as an ecological alternative method of dyeing polyester because it avoids phenolic compounds. Moreover, it can be recommended to the industry due to the antimicrobial, antioxidant, biodegradability and anti-mutagenic activities of the carriers involved.


All these allotypes are ligands for the T cell receptor and leukocyte immunoglobulin-like receptors, but additionally, the HLA-B allotypes are ligands for the killer-cell immunoglobulin-like receptor family member KIR3DL1, HLA-C*0602 is a ligand for KIR2DL1, and HLA-C*0702 is a ligand for KIR2DL2/3. Using super-resolution microscopy, we found that both HLA-B and HLA-C formed more clusters and a greater proportion of HLA contributed to clusters, when expressed at lower levels. Thus, HLA class I organization is a covariate in genetic association studies of HLA class I expression level with disease progression. Surprisingly, we also found that HLA-C was more clustered than HLA-B when expression level was controlled. HLA-C consistently formed larger and more numerous clusters than HLA-B and a greater proportion of HLA-C contributed to clusters than for HLA-B. We also found that the organization of HLA class I proteins varied with cell type. T cells exhibited a particularly clustered organization of HLA class I while B cells expressed a more uniform distribution. In summary, HLA class I variants are organized differently in the cell surface membrane which may impact their functions.The particular HLA class I variants an individual carries influences their resistance and susceptibility to a multitude of diseases. Expression level and variation in the peptide binding region correlates with, for example, a person's progression to AIDS after HIV infection. One factor which has not yet been addressed is whether or not different HLA class I proteins organize differently in the cell membrane on a nanoscale. Here, we examined the organization of three HLA-B allotypes (B It has been proposed that HLA diversity, which includes a high frequency of non-synonymous mutations in the peptide-binding region compared to elsewhere in the genome, is driven by selection pressure from pathogens that provide peptides to CTL (HLA class I (HLA-I) proteins are a critical component of the human immune response –4. Classs to CTL , 6. KIR s to CTL , 8.*2705/B*57, this is associated with a slower progression to AIDS  were transfected into 721.221 cells that lack expression of classical HLA-I  and expressed this construct in 721.221 cells . Lymphocyte populations were isolated and cultured as previously described, as were 721.221 cells  with the*2705/B*5301/B*5701  or HLA-B*57 . These and pcDNA3.1 expression vectors containing HLA-C*0602 /C*0702  pre-coated with 0.01% poly-L-lysine (Sigma-Aldrich). Cells were incubated on the glass for 20 min at 37°C, fixed in 4% formaldehyde for 20 min at room temperature (RT), then washed in PBS. Samples were then blocked for 1 h in 4% BSA/PBS and stained with AlexaFluor647-conjugated W6/32 mAb in 1% BSA/PBS for 1 h at RT. Samples were then washed in PBS, fixed again by 5 min incubation in 4% formaldehyde before a final wash. TIRF microscopy and STORM images were obtained with a wide-field SR GSD 3D microscope, as previously described . Forty tSTORM images were processed as previously described . Poorly In the Ripley's K function analysis , each evSpatial point pattern analyses have been previously described  with RipThe raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.PK performed experiments, supervised the project, analyzed data, and prepared the manuscript. CB conceived the project, performed experiments, and analyzed data. DW developed analysis methods. DD conceived and supervised the project and prepared the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Milk characteristics in terms of volatile compounds can influence the subsequent product characteristics and can give indications about metabolism. These features can strongly depend on feeding and management. In this perspective, the screening of milk samples intended for Parmigiano-Reggiano and Grana Padano located in Northern Italy was performed, focusing on a panel of volatile molecules. The work was carried out on a total number of 25 bovine milk samples for the production of Parmigiano-Reggiano and Grana Padano. Milk samples were collected from May to September and analyzed for volatile molecules using headspace gas chromatography mass spectrometry. A range of several volatile molecule classes was considered . Results showed a significant influence of the month and destination of milk due to the time period and subsequent use in cheesemaking (Parmigiano-Reggiano and Grana Padano). Significant differences between months were observed for all volatiles. These preliminary results indicate differences between the two types of milk due to the period and destination. The study of volatile molecules in milk will give important information about the physiology of milk and the evolution of dairy products. These features must be extended and confirmed by the sensory analysis of milk and derived products, leading to a more complete characterization of milk biology and derived products. Ruminant milk has different physical and chemical characteristics involving differences in sensory properties . Flavor There is a complex interaction between animal metabolism, the environment, and the involved molecules implied in the definition of flavor and aroma ; milk arWhile it is well-known that small ruminant milk differs from cow milk, discrepancies can be appreciated also between bovine breeds. Flavor compounds derive from fodder (direct passage), rumen microbial environment, gastrointestinal tract, general metabolism, and udder. Furthermore, some components are inhaled by the process of eructation .-Carbohydrates.-Aminoacids.-Fatty acids.-Terpenes.-Other moleculesVolatile molecules can derive from several sources, as :-CarbohyAlthough, several classes of compounds have been detected in raw milk ,4,5,6.During the physiological modification timelapse, a number of class of molecules have been identified, such as alcohols, ketones, esters, acids, solforates, and hydrocarbons. These molecules present a common presence in the milk samples and species-specific compounds . The volAiming to evaluate more precisely the milks for the production of Grana Padano and Parmigiano-Reggiano from the volatile point of view, a study taking into account the milk destination and the season (spring-summer) was performed by analyzing the volatile content of milk, determined by headspace solid phase micro-extraction gas chromatography mass spectrometry.A total number of 25 Holstein lactating cows was enrolled: In total, 15 subjects came from three farms for the production of Grana Padano cheese , 10 from two farms producing milk for the caseification of Parmigiano Reggiano . All cows were multiparous  and submitted to regular veterinary examinations. At the moment of the visit, the cows were considered healthy. Cows were submitted to diets adherent to the disciplinary prescribed to each cheese production. The summary data for each diet are shown in Milk samples were collected every month of the year 2017 in the first teen days of the spring-summer months (May-September), during the morning milking. Samples were refrigerated (4–5 °C) soon after collection and carried to the laboratory for the analysis.2-Methylbutanal.2-Propanone.2-Butanone.2,3-Butanedione.2,3-Heptanedione.2-Heptanone.3-Hydroxy-2-butanone.2-Hydroxy-3-pentanone.2-Nonanone.5-Hydroxydimehyl-4-octanone.Methanol.2-Propanol.Ethanol.2-Methyl-1-propanol.4-Methyl-2-pentanol.1-Butanol.2-Hexanol.3-Methyl-1-butanol.3-Methylbut-3-ene-1-ol (isoprenol).1-Pentanol.3-Methyl-2-butenol.3-Pentanol.1-Hexanol.2-Hydroxy-3-pentanone 2.1-Octen-3-ol.1-Octen-4-ol.2-Heptanol.2,3-Butanediol.2-Butanol.Benzene ethanol.4-Methyl-2-oxovaleric acid.Ethanoic acid.2-Methylpropanoic acid.Butanoic acid.Pentanoic acid.Hexanoic acid.Acetic acid methylester.Ethylacetate.Butyric acid methyl ester.Methyl-3-metilbutanoate.Acetic acid ethylenester.Isoamylacetate.Hexanoic acid ethyl ester.Hexanoic acid methyl ester.Isoamyl-n-butyrate.Octanoic acid methyl ester.Methyldecanoate.Butyric acid-3-methyl ester.Hexanoic acid methylester.o-Xylene.p-Xylene.Dimethylsulfide.Dimethylsulfoxide.Dimethylsulfone.A panel of 54 volatile molecules was determined by headspace analysis as described by Panseri et al. ; the molAldehydes.Ketones.Alcohols.Acids.Esters.Aromatic hydrocarbons.Solforates.All molecules were subdivided into seven classes, defined following the chemical classes of:The volatile compounds of milk were analyzed according to Panseri et al. . The tecAll milk samples were prepared by weighing exactly 10 mL of milk sample in a 20 mL glass vial, fitted with a cap equipped with silicon/polytetrafluoretylene (PTFE) septa , and by adding 1 mL of the internal standard solution (IS) in water (4-methyl-2-pentanone 1 µg/mL). At the end of the sample equilibration period (1 nh), a conditioned (1.5 h at 280 °C) 1 µL carboxentm/polydimethylsiloxane (CAR/PDMS) StableFlextm fiber  was exposed to the headspace of the sample for extraction (3 h) by a CombiPAL system injector autosampler . A temperature of 4 °C was selected as the extraction temperature in order to prevent possible matrix alterations . To keep a constant temperature during analysis, the vials were maintained on a heater plate .-1. The transfer line to the mass spectrometer was maintained at 230 °C, and the ion source temperature was set at 250 °C. The mass spectra were obtained by using a mass selective detector with the electronic impact at 70 eV, a multiplier voltage of 1456 V, and by collecting the data at rate of 1 scan s−1 over the m/z range of 30–350. Compounds were identified by comparing the retention times of the chromatographic peaks with those of authentic compounds analyzed under the same conditions when available, or by comparing the Kovats retention indices with the literature data. The identification of MS fragmentation patterns was performed either by comparison with those of pure compounds or using the National Institute of Standards and Technology (NIST) MS spectral database. The quantitative evaluation was performed using the internal standard procedure, assuming a response factor of one. Thus, the quantitative results (µg/g) of each volatile compound were computed by relating the peak intensity of the volatile compounds to the intensity of the internal standard added to the sample in a known amount.HS-SPME analysis was performed using a Trace GC Ultra  Gas Chromatograph coupled to a quadrupole Mass Spectrometer Trace DSQ  and equipped with an Rtx-Wax column . The oven temperature program was: From 35 °C, hold 8 min, to 60 °C at 4 °C/min, then from 60 °C to 160 °C at a rate of 6 °C/min, and finally from 160 °C to 200 °C at a rate of 20 °C/min. Carry over and peaks originating from the fiber were regularly assessed by running blank samples. After each analysis, fibers were immediately thermally desorbed in the GC injector for 5 min at 250 °C to prevent contamination. The injections were performed in splitless mode (5 min). The carrier gas was helium at a constant flow of 1 mL miny’ is the new value of the variable, y is the original value. The term, y + 1, was introduced in order to avoid errors in the logarithm calculation due to zero values.Data are shown as mean ± standard deviation. The effects of the cheese destination and month of collection were analyzed by the two-way analysis of variance (ANOVA), considering destination and season as fixed factors. Due to the nature of the data, ANOVA was performed on natural logarithmic data, calculated as:In order to evaluate possible multivariate differences between the destination and month groups, a multiple factor analysis (MFA), considered as an evolution of principal component analysis, was applied. In this way, multivariate relations between two groups of qualitative variables (month and milk destination) and a group of response variables (volatile classes) were calculated.p < 0.05.In order to better refine the characteristics of the volatile composition of milks, the MFA procedure was extended—for Padano and Reggiano groups—to all volatiles, putting in evidence the correlations of all variables with the first two dimensions, and the correlations of groups with the abovementioned dimensions. Single volatile milk contents were processed by a two-way ANOVA on log-values as described. Month and milk destination were set as fixed factors, and the statistical significance was set at The univariate descriptive statistics and the ANOVA were performed with the software, JMP ver. 7.0, while the MFA analysis with the software, R ver. 3.5.2. for Windows platform.The mean values, the standard deviations, and the effects calculated by ANOVA are reported in ANOVA results underline that the period, at least in the spring-summer period, has a deep influence on the volatile levels, since each volatile class differs significantly during that time. In particular, aldehydes, acids, and aromatic hydrocarbons increased in concentration in late spring-early summer, while ketones, alcohols, esters, and solforate molecules reached their zenith in August . When miThe MFA analysis put in evidence the interesting multivariate relation between volatile classes and factors; the first two dimensions explain 39.88% of the total variability . Grana PThe univariate distinction between the two different destination milks is repeated also with MFA: Grana Padano samples tend to be richer in solforates and aromatic hydrocarbons with respect to Parmigiano-Reggiano milk, characterized mainly by an abundance of aldehydes. The two milk groups are quite well separated by the zero abscissa of the two-dimensional component plan.The MFA data processing put in evidence again the sharp separation between the milk groups. By examining dimension 1, Parmigiano milk correlates well with the presence of several esters, alcohols, and ketones, whereas Padano milk appears inversely related with these components . The negDimension 2 relates positively with Padano milk, except for methanol . Also, iThe effects of the month of production and destination on the single volatile levels are reported in This survey on the volatile characteristics of milk destined for the production of two broadly diffused cheeses evidences differences between products before transformation. From our results, the year period can heavily influence the abundance or lack of substances, according to . The preParmigiano-Reggiano milk contains higher concentrations of aldehydes, alcohols, and acids than milk destined for Grana Padano production. Aldehydes as a pentanal can contribute to the appearance of grassy aromas, mainly when feed derives from pastures with respect to concentrates . An inveThe picture of volatile compounds for milk before cheesemaking could add precious information about the physiological condition of animals, as well as help to make forecasts on the evolution of transformed products, as a predictor of cheese quality and characterization.


The proposed device achieves an enhanced power conversion efficiency (PCE) of 9.33% compared with the device based on the high-temperature sol-gel-processed ZnO selective layer, which achieves a PCE of 8.62%. The inverted device also shows good stability, keeping more than 82% of its initial PCE after being stored under ambient air conditions and a humidity of around 40% without any encapsulation for 240 h. The results show the potential for the fabrication of efficient non-fullerene OSCs with low-temperature metal oxide selective layers.In this work, an efficient inverted organic solar cell (OSC) based on the non-fullerene PBDB-T:IT-M blend system is demonstrated by using an aqueous solution processed ZnO electron-selective layer with the whole process temperature ≤150 °C and a thermally evaporated MoO The inverted device also shows good stability, which could maintain 82% of its initial PCE after 240 h storage when exposed under ambient air conditions and a humidity of around 40% without any encapsulation.In this work, we have employed the aqueous-solution-processed ZnO film as the electron-selective layer and the thermally evaporated MoOacceptor ,41. In t3)42+ solution, then the solution was refrigerated for more than 12 h after being ultrasonically processed for 10 min. For the sol-gel solution, we dissolved zinc acetate dehydrate Zn(CH3COO)2·2H2O  in anhydrous ethanol CH3CH2OH  and added ethanolamine as the stabilizer followed by stirring for over 12 h at 60 °C. PBDB-T and IT-M were purchased from Dalian Lichuang Chemical Co., Ltd., Dalian, China. PBDB-T and IT-M were dissolved in a chlorobenzene/1,8-diiodoctane (99:1 vol %) mixed solvent with a total concentration of 20 mg/mL and 1:1 weight ratio. The solution of PBDB-T and IT-M was stirred at 100 °C for 12 h before use. ITO-coated glass substrates have a sheet resistance about 10 Ω/sq. The chlorobenzene and MoO3 were supplied by Sigma-Aldrich. All the materials were used without any further purification.For the aqueous precursor solution, we dissolved ZnO powder  in ammonia  to form a 0.1 M Zn, deionized water, acetone, and ethanol in an ultrasonic bath for about 20 min, and then were blown dry by a nitrogen gun  before use. Next, the ITO-coated glass substrates were treated by UV ozone for 15 min. Following that, a layer of ZnO, using ZnO aqueous solution, was spin-coated on cleaned ITO glass substrates at 3000 rpm for 45 s and then annealed on a heating stage at 150 °C for 15 min under ambient conditions. For comparison, the devices with the ZnO layer spin-coated by sol-gel method at 3000 rpm for 60 s were also fabricated, and then annealed at 275 °C for 15 min. The PBDB-T:IT-M solution was spin-coated on the ZnO films at 3000 rpm for 120 s to obtain the optimal film thickness of ≈100 nm in a nitrogen-filled glove box, followed by annealing in N2 at 150 °C for 30 min. Finally, 8 nm MoO3 and 100 nm Ag were deposited via a thermal evaporation method through a metal shadow mask in a vacuum chamber. The device area of the solar cells is 70 mm2.The inverted devices based on PBDB-T:IT-M have been prepared in this work. As shown in All the current density–voltage (J–V) characteristics of devices were obtained under a Keithley 2400 source measure unit  and a Xenon lamp with an AM 1.5G filterXES-301, SEN-EI Electric. Co. Ltd, Osaka, Japan). All the Incident photo-to-electron conversion efficiency (IPCE) was measured through a quantum efficiency measurement system . The thin films morphology was gained through the tapping mode atomic force microscopy (AFM) tests by an Agilent 5500 scanning probe system . The transmittance spectra were investigated by UV–Vis/NIR spectrophotometer . The photoluminescence (PL) spectrum was also utilized to analyze the ZnO thin film using the 325 nm line of a He–Cd laser .3 acts as the role of both the hole-selective layer and the electron-blocking layer and can effectively enhance the transportation of holes into the anode.The corresponding energy band diagram of the inverted OSCs is illustrated in 2 light intensity, are shown in J0 is the saturation current, phJ the photocurrent, sR the series resistance, shR the shunt resistance, n the ideality factor, q the electron charge, Bk the Boltzmann constant, and T the temperature, respectively. By combining Equation (1) with the proposed explicit analytic expression method [VOC) of 0.88V, a short current density (JSC) of 16.54 mA/cm2, and a fill factor (FF) of 63.84%. For the comparison, the device with the ZnO layer spin-coated by the sol-gel solution achieves a best PCE of 8.62% with a VOC of 0.87 V, a JSC of 15.30 mA/cm2, and a FF of 64.35%. By comparing the devices with the ZnO layer deposited by the aqueous solution and the sol-gel solution, there are no obvious differences for VOC and FF. The device with the ZnO layer deposited by aqueous solution has a relative high JSC when compared with that deposited by the sol-gel method. Furthermore, the enhanced JSC results in an improved PCE for the the device with the ZnO layer deposited by aqueous solution, as shown in The obtained light J–V curves of the inverted OSCs, which are tested under AM 1.5G illumination with 100 mW/cmn method , the expJSC based on the IPCE curve is 16.14 mA/cm2 for the ZnO using aqueous solution and 15.31 mA/cm2 for the ZnO using sol-gel solution, respectively; only slightly lower than their measured JSC (16.54 and 15.30 mA/cm2). The devices degradatedduring the IPCE test in the air atmosphere can probably explain this result. Anyway, the IPCE measurement results again confirm that the device based on aqueous solution has a higher JSC than that based on sol-gel solution.For further comparison, the IPCE curves of the devices based on aqueous solution and sol-gel solution are shown in VOC and FF change slowly. After 240 h storage, the PCE of the inverted OSCs still remains above 82% of its initial value, which exhibits that the inverted device has a high stability.Besides the PCE, the stability of the inverted OSCs with the aqueous-solution-processed ZnO layer is also investigated. We stored these devices under ambient air conditions and a humidity of around 40% without any encapsulation. The variations of the performance parameters at different storage times are shown in The photoluminescence measurement was adopted to characterize the properties of ZnO films. AFM was adopted to characterize the morphology of the ZnO films. Contact angle measurement was carried out to detect hydrophilicity of the ZnO films, which will affect the formation of the active layer. Generally, if the contact angle of a material is less than 90°, then we consider it to be hydrophilic, whereas if the contact angle is greater than 90°, we consider it to be hydrophobic. The smaller the contact angle, the better the hydrophilicity. The result is shown in 3 carrier selective layers in this work. The low-bandgap material PBDB-T is employed so that more solar light can be harvested by the device; the non-fullerene acceptor named IT-M is adopted to enhance the absorption ability of the Acceptor/Donor system; and a ZnO selective layer is deposited by aqueous solution and prepared in a low-temperature process, so that it can be compatible with the roll-to-roll process. The proposed device achieves an enhanced PCE of 9.33% compared with the device based on the sol-gel-processed ZnO selective layer, which achieves a PCE of 8.62%. The inverted device shows good stability, keeping more than 82% of its initial PCE after being stored for 240 h. The result shows a promising potential for the fabrication of efficient, flexible non-fullerene organic solar cells with low-temperature metal oxide selective layers.OSCs based on PBDB-T:IT-M were fabricated with low-temperature-processed ZnO and MoO